Python的MLPClassifier值错误

Question

我目前正试图训练MLPClassifier在sklearn实现... 当我尝试使用给定值我得到这个错误训练它：

ValueError异常：设置有一个数组元素序列。

的feature_vector的格式

[one_hot_encoded名优产品]，[不同的应用程序扩展到均值为0，方差为1]

有谁知道我做错了吗？

谢谢！

feature_vectors：

[

阵列（[0，0.1，0.1，0.1，0.1，0.1，0.1，0。 ，0.0,0.0,0.0,0.1,0.3,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0， 0。，0，0 ，0.1，0.1，0.1，0.1，0.1，0.1，0.1，0.1，0.1，0.1，0.1，0.1，0.1 ， 0.,0,0,0,0,0,0,0,0,1,0,0,0,01,35,164,106,1745,0。，0，0，0，0，0，0，0，0，0，0，0，0，0 ，0.1，0.1， 0。，0，0，0，0，0，0，0，0，0，0 ，0.1，0.1，0.1，0.1，0.1，0 。，0。，0，0，0]），

阵列（[0.82211852，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818， -0.22976818，-0.22976818，4.45590895，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818 ，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818， - 0.22976818， -0.22976818，0.3439882，-0.22976818，-0.22976818，-0.22976818， 4.93403927，-0.22976818，-0.22976818，-0.22976818，0.63086639， 1.10899671，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，1.58712703，-0.22976818， 1.77837916，-0.22976818， - 0.22976818，-0.22976818，-0.22976818, -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，2.16088342，-0.22976818，2.16088342， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818， -0.22976818，-0.22976818，9.42846428，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， 0.91774459，-0.22976818，-0.22976818，4.16903076，-0.22976818， -0.22976818，-0.22976818，-0.22976818 ，-0.22976818,2.444776161, -0.22976818，-0.22976818，-0.22976818,1.96963129,1.96963129, 1.96963129，-0.22976818，-0.22976818，-0.22976818，-0。22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，7.13343874， 5.98592598，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， 3.02151799，4.26465682 ，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818， - 0.22976818，-0.22976818，-0.22976818， -0.22976818，2.25650948，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818 ，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818， - 0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， 1.30024884，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818， - 0.22976818，-0.22976818，-0.22976818, -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818 ，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，4.74278714，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818 ，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818， - 0.22976818，-0.22976818，-0.22976818, -0.22976818，-0.22976 818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，0.3439882， -0.22976818，0.3439882，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818， - 0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，0.53524033，-0.22976818， - 0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22 976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，3.49964831， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818]）

]

g_a_group：

[0.0.0.0.0.0.0.0.0.0.0。]

MLP：

从sklearn.neural_network进口MLPClassifier

CLF = MLPClassifier（解算器= 'lbfgs'，α-= 1E-5， hidden_​​layer_sizes =（5,2 ），random_state = 1）

clf.fit（feature_vectors，g_a_group）

Answer 1

您的数据对于调用.fit调用中期望得到的结果没有任何意义。特征向量被认为是大小N x d，其中N的矩阵 - 数的数据点和d数目的特征，和第二个变量应该保持的标签，因此它应该是长度N（或N x k其中k的矢量是每个点的输出/标签数量）。无论在变量中表示什么 - 它们的大小与他们应该表示的大小不匹配。